Preset Time Research Articles

Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors.

This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefined-time convergence property guarantees that the regulation task can be finished by robotic manipulators in a preset time, in spite of the initial state of manipulators. This feature will facilitate the scheduling of a series of tasks in industrial applications. To this end, a varying-parameter predefined-time convergent zeroing neural dynamics (ZND) model is first proposed and employed to solve the regulation problem. As well as the primary task, a conventional ZND model is utilized to achieve the avoidance of obstacle. The stability of the proposed controller is analyzed based on the Lyapunov stability theory. For the sake of dealing with the unknown kinematic model of robotic manipulators, gradient neural dynamics (GND) models are exploited to adapt the Jacobian matrices just relying on the control signal and sensory output, which enables us to control robotic manipulators in a model-free manner. Finally, the efficacy and merits of the proposed control method are verified by simulations and experiments, including a comparison with the existing method.

Prescribed Performance Control for Teleoperation System of Nonholonomic Constrained Mobile Manipulator Without Any Approximation Function

The teleoperation system of mobile manipulator has been widely applied for space exploration, medical assistance, and other fields. To improve operational efficiency, this paper presents a novel prescribed performance synchronization control scheme without applying any approximation functions for a class of teleoperation system of the mobile manipulator with time-varying delays and nonholonomic constraints. Compared with the general manipulator system, the mobile manipulator has a larger workspace and stronger operability. Yet, the heterogeneity of master-slave robots and the nonholonomic constraint of slave robot bring many difficulties to the control of the system. Firstly, a prescribed performance function (PPF) is designed to guarantee that the position synchronization errors are remained within the predetermined boundary and converge to a small preset area within a preset time. Then, the direct force feedback information is introduced into the controller to improve the transparency of the teleoperation. In addition, there are no approximation functions applied in the proposed controller, which can significantly reduce the complexity and computation of the system. Finally, simulation and experimental results are made to illustrate the availability of this method for practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to develop an algorithm to improve the steady-state and transient-state performance of mobile manipulator teleoperation system without increasing the complexity of the controller. Compared with the existing works, in this paper, the control algorithm without any approximators is proposed and a time-varying prescribed performance function is introduced, which promotes the steady-state and transient-state performance of the system essentially. At the same time, the introduction of force feedback information improves the transparency of the teleoperation system. The feasibility and effectiveness of the algorithm are verified on UR5e-Husky experimental platform. In the future, we will strive to further improve the transparency of the mobile manipulator teleoperation system in order to achieve the immersive effect of operator.

The Ethical Obligation for Research During Public Health Emergencies: Insights From the COVID-19 Pandemic

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise—i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention—has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

Dental pulp cells cocultured with macrophages aggravate the inflammatory conditions stimulated by LPS

BackgroundPulp inflammation is complex interactions between different types of cells and cytokines. To mimic the interactions of different types of cells in inflamed dental pulp tissues, dental pulp cells (DPCs) were cocultured with different ratios of macrophages (THP-1) or LPS treatment.MethodsDPCs were cocultured with various ratios of THP-1, then photographed cell morphology and determined cell viability by MTT assay at preset times. Total RNA was also extracted to measure the inflammation marker-IL-6 and IL-8 expressions by RT-Q-PCR. The DPCs and THP-1 were treated with 0.01 – 1μg/ml lipopolysaccharide (LPS) and extract RNA at preset times, and detected IL-6 and IL-8 expression. DPCs were cocultured with various ratios of THP-1 with 0.1 μg/mL LPS, and detected IL-6 and IL-8 expression after 24 and 48 h. The data were analyzed by unpaired t-test or Mann-Whitney test. Differences were considered statistically significant when p < 0.05.ResultsTHP-1 and DPCs coculture models did not suppress the viability of DPCs and THP-1. Cocultured with various ratios of THP-1 could increase IL-6 and IL-8 expressions of DPCs (p = 0.0056 - p < 0.0001). The expressions of IL-6 and IL-8 were stronger in higher ratio groups (p = 0.0062 - p < 0.0001). LPS treatment also induced IL-6 and IL-8 expressions of DPCs and THP-1 (p = 0.0179 – p < 0.0001 and p = 0.0189 – p < 0.0001, separately). Under the presence of 0.1 μg/mL LPS, DPCs cocultured with THP-1 for 24 h also enhanced IL-6 and IL-8 expression (p = 0.0022). After cocultured with a higher ratio of THP-1 for 48 h, IL-6 and IL-8 expressions were even stronger in the presence of LPS (p = 0.0260).ConclusionsCoculturing dental pulp cells and macrophages under LPS treatment aggravate the inflammatory process. The responses of our models were more severe than traditional inflamed dental models and better represented what happened in the real dental pulp. Utilizing our models to explore the repair and regeneration in endodontics will be future goals.

Improved prescribed performance fuzzy control for nonlinear multiagent systems with actuator faults and dual input saturations

This paper conducts the distributed adaptive prescribed performance fuzzy control design for nonlinear multiagent systems (MASs) subjected to actuator faults and dual input saturations. A unified smooth characterizing function is constructed to approximate a practical input saturations model. Then, with the aid of the mean-value theorem, Nussbaum-type function and boundary estimation method, the distributed saturated fault-tolerant controllers are devised to deal with actuator faults and dual input saturations. In order to achieve excellent tracking performance of followers, an improved prescribed performance control method with a tight constraint region is proposed. Different from most existing control methods, the proposed method not only ensures that the tracking error rapidly converges to the specified area within a predetermined time, but also achieves transient performance without obvious overshoot. Stability analysis proves that the consensus tracking error converges to an arbitrary small specified area within a preset time and all signals of the overall controlled systems are semi-globally uniformly ultimately bounded (SUUB). Eventually, the comprehensive simulation results validate the feasibility and superiority of the control strategy.

Optimized Adaptive Fuzzy Security Control of Nonlinear Systems With Prescribed Tracking Performance.

This article studies the optimized fuzzy prescribed performance control problem for nonlinear nonstrict-feedback systems under denial-of-service (DoS) attacks. A fuzzy estimator is delicately designed to model the immeasurable system states in the presence of DoS attacks. To achieve the preset tracking performance, a simper prescribed performance error transformation is constructed considering the characteristics of DoS attacks, which helps obtain a novel Hamilton-Jacobi-Bellman equation to derive the optimized prescribed performance controller. Furthermore, the fuzzy-logic system, combined with the reinforcement learning (RL) technique, is employed to approximate the unknown nonlinearity existing in the prescribed performance controller design process. An optimized adaptive fuzzy security control law is then proposed for the considered nonlinear nonstrict-feedback systems subject to DoS attacks. Through the Lyapunov stability analysis, the tracking error is proved to approach the predefined region by the preset finite time, even in the presence of DoS attacks. Meanwhile, the consumed control resources are minimized due to the RL-based optimized algorithm. Finally, an actual example with comparisons verifies the effectiveness of the proposed control algorithm.

Temporal patterns of gene expression in response to inoculation with a virulent Anaplasma phagocytophilum strain in sheep

The aim of this study was to characterize the gene expression of host immune- and cellular responses to a Norwegian virulent strain of Anaplasma phagocytophilum, the cause of tick-borne fever in sheep. Ten sheep were intravenously inoculated with a live virulent strain of A. phagocytophilum. Clinical-, observational-, hematological data as well as bacterial load, flow cytometric cell count data from peripheral blood mononuclear cells and host’s gene expression post infection was analysed. The transcriptomic data were assessed for pre-set time points over the course of 22 days following the inoculation. Briefly, all inoculated sheep responded with clinical signs of infection 3 days post inoculation and onwards with maximum bacterial load observed on day 6, consistent with tick-borne fever. On days, 3–8, the innate immune responses and effector processes such as IFN1 signaling pathways and cytokine mediated signaling pathways were observed. Several pathways associated with the adaptive immune responses, namely T-cell activation, humoral immune responses, B-cell activation, and T- and B-cell differentiation dominated on the days of 8, 10 and 14. Flow-cytometric analysis of the PBMCs showed a reduction in CD4+CD25+ cells on day 10 and 14 post-inoculation and a skewed CD4:CD8 ratio indicating a reduced activation and proliferation of CD4-T-cells. The genes of important co-stimulatory molecules such as CD28 and CD40LG, important in T- and B-cell activation and proliferation, did not significantly change or experienced downregulation throughout the study. The absence of upregulation of several co-stimulatory molecules might be one possible explanation for the low activation and proliferation of CD4-T-cells during A. phagocytophilum infection, indicating a suboptimal CD4-T-cell response. The upregulation of T-BET, EOMES and IFN-γ on days 8–14 post inoculation, indicates a favoured CD4 Th1- and CD8-response. The dynamics and interaction between CD4+CD25+ and co-stimulatory molecules such as CD28, CD80, CD40 and CD40LG during infection with A. phagocytophilum in sheep needs further investigation in the future.

Adaptive NN formation tracking control for the multiple underactuated USVs with prescribed performance and input saturations

A novel neural network adaptive prescribed performance formation control algorithm is investigated for multiple underactuated unmanned surface vehicles (USVs) with asymmetric input saturations and unknown external disturbances. Initially, a new prescribed performance function is adopted to ensure that USV formation errors reach and keep within a predetermined range at a preset time. Then, the underactuated problem of USVs is addressed by an additional term generated by a Nussbaum function, and the model uncertain dynamics set is compensated by the radial basis function neural network (RBF NN) minimum parameter method. Meanwhile, adaptive techniques are employed to deal with external disturbances and unknown approximation errors. Furthermore, a continuously differentiable asymmetric saturation model based on the Gaussian error function is introduced to design the controller, eliminating the influence of non-smooth input saturations. Finally, the boundedness of all signals in the closed-loop system is given by the Lyapunov stability theory proof, and contrast simulation experiments verify the effectiveness and superiority of the proposed algorithm.

RELAY COORDINATION PLANNING FOR HIGH PHOTOVOLTAIC PENETRATION USING PRESET-TIME METHOD

In light of the advancement and technological development, electricity has become the main component of all areas of life. Photovoltaic (PV) systems are widely used within distribution networks. Despite all the advantages that PV possesses, it has some effects on the protection system. One of the most essential effects caused by PV is an increase in the relay operating time (ROT) of the backup protection relay during faults. Many studies have focused on improving the coordination of protection relays at high PV penetration, either by using current limiters or modifying the characteristics of the protection curves. However, they require a high cost and advanced relay. Therefore, the Preset-Time method is introduced in this study to shorten the ROT of the backup protection relay within the radial distribution networks which contributes to accelerating the removal of faults. The proposed method is tested on the Iraqi distribution network and is simulated using ETAP 19 software. The performance of the Preset-Time method is evaluated during the change in penetration levels. To validate the preset time method, its results are compared to the conventional method and the Max PSM method. From the results obtained by the Preset-Time method, the highest percentage of reduction in ROT was 33.20% and 29.23% compared to the conventional and the Max PSM methods, respectively.

Consensus tracking iterative learning control of second-order multi-agent systems

In this paper, the problem of consensus tracking control for a class of second-order leader-following nonparametric uncertain multi-agent systems, which perform a given repetitive task over a finite interval with arbitrary initial error. By means of learning control and initial shift rectifying, a first-order attractor control algorithm is presented.In the tracking process, the proposed algorithm simultaneously rectifies all the initial state shifts, and after enough iterations, the all following multi-agents’ states perfectly track the leader’s state in the preset time interval. Finally, simulation results demonstrate the effectiveness of the learning control algorithm.

Minimal-order specified-time unknown input observers

This paper presents a framework on minimal-order specified-time unknown input observers for linear systems based on a pairwise observer structure. A minimal-order specified-time observer is first proposed for the linear system without the unknown input, which can estimate the state exactly at the preset time by seeking for the unique solution of a system of linear equations. To further release the computational burden, another form of the specified-time observer is designed. In the presence of the unknown input, it is shown that the existing reduced-order specified-time unknown input observer is not the minimal-order one. By introducing a singular transformation to decouple the effect of the unknown input, the minimal-order specified-time unknown input observer is presented, with the strong* observability capturing the sufficient and necessary condition. The robustness of the proposed observers is also discussed.

Adaptive neural optimized control for uncertain strict-feedback systems with unknown control directions and pre-set performance

In this paper, we develop an adaptive neural strategy based on optimized backstepping (OB) technology for strict-feedback systems with unknown control directions and pre-set performance. First, the OB technique is used to construct each optimized virtual controller and optimized actual controller in backstepping, so as to achieve global optimization. Second, the problem of unknown control directions is overcome by using a Nussbaum-type function. Next, a time-varying switching function and the quartic barrier Lyapunov function are introduced to solve the control problem of the pre-set time and accuracy. The proposed control strategy makes the tracking error of the system converge to the pre-set accuracy within the pre-set time. At the same time, all of the signals of the closed-loop system are bounded. Finally, two set simulations verify the effectiveness of the proposed control strategy.

Parameter-dependent functional observer-based finite-time adaptive memory ISMC for continuous LPV systems with unknown nonlinear functions

This paper investigates a parameter-dependent (PD) functional observer-based finite-time adaptive memory integral sliding mode controller (ISMC) scheme for continuous linear parameter varying (LPV) systems with unknown nonlinear functions. First, a novel functional observer including parameters and parameter rates is proposed, and the observer gains can be directly designed. Different from the previous studies, a memory ISMC scheme with a time-varying delay through the functional observer is used for the first time in LPV systems to improve the performance. Moreover, to fully adopt the model characteristics of LPV systems and the functional observer, an integral PD surface function that contains PD input matrices and a memory parameter is established simultaneously. And then, an adaptive compensator is deduced for eliminating the influence of unknown nonlinear functions, so that the designed memory ISMC can make closed-loop LPV systems stable within a preset finite time. Furthermore, stability criteria with less conservatism for the LPV systems are proposed by memory Lyapunov–Krasovskii functional with measurable parameters. These criteria can guarantee LPV systems are finite-time boundedness with performance (FTB-) on the sliding stage, arriving stage and entire finite-time region. Finally, a riderless bicycle is given to display the validity and superiority of the developed approach.

Digitization accuracy and scannability of different prosthodontic materials: An in vitro trial

Digital scanning of different prosthodontic materials is commonplace in contemporary practice. However, the scannability of prosthodontic materials has not been thoroughly investigated. The purpose of this in vitro study was to evaluate the scanning accuracy and measure the unscanned area in a preset time limit of commonly used framework materials. A mandibular acrylic resin reference dental typodont with 3 teeth, with the central one prepared for a complete coverage crown, was digitized by using a desktop scanner. A complete coverage crown was generated in standard tessellation language (STL) format. Three groups were created from the digital design according to the crown material: milled polyetheretherketone (PEEK), milled airborne-particle abraded titanium, and milled polymethylmethacrylate (PMMA). They were scanned with the desktop scanner to be used as reference files for each group. The intraoral scanner Medit i700 was used to digitize each specimen 10 times (n=10). Using a nonmetrology grade software program, the deviations between the test STL file of the intraoral scanner and the reference STL file of the desktop scanner were assessed by using the RMSvalues. The unscanned surface area in a preset time limit of 6seconds (scannability) was assessed. Groups were compared by using 1-way ANOVA followed by the Tukey post hoc test with Bonferroni correction when theresults were significant. All tests were 2-tailed (α=0.05). Regarding deviation analysis, RMS discrepancies were computed, and significant differences in trueness were found (P<.001) among the 3 studied groups. The titanium group had the highest trueness followed by the PEEK and PMMA groups, which were statistically similar (P>.05). Precision differed significantly among the 3 studied groups (P<.001). PEEK was the most precisely scanned material followed by titanium, and the PMMA group had the least precision. Regarding scannability, there were overall significant differences (P<.001). Titanium was the most scannable, followed by PEEK and then PMMA. Airborne-particle abraded titanium had better trueness and scannability than PEEK and PMMA. However, PEEK was the most precisely scanned material.

Event-Triggered Prespecified Performance Control for Steer-by-Wire Systems With Input Nonlinearity

This paper addresses the prescribed tracking performance control problem for uncertain steer-by-wire (SbW) systems with input nonlinearity (including dead-zone and actuator fault) and the limitation of controller-area-network (CAN) bandwidth. An adaptive interval type-2 fuzzy logic system (IT2 FLS) is introduced to approximate the lumped model uncertainty, and a switching event-triggering mechanism (ETM) is applied to save the communication resources. Combining the backstepping approach and barrier Lyapunov function techniques, a prescribed tracking performance control method is proposed for SbW systems, where the initial values of state errors are no longer required in the controller design. Theoretical analysis shows that the tracking error can converge to the predefined residual set within preset time instead of the time tending infinite, while the closed-loop system is semi-globally stable. Simulations and vehicle experiments are presented to verify the effectiveness of the proposed control method.

Usefulness of preset count acquisition in pediatric 99m Tc-DMSA planar imaging.

This study was aimed at determining the minimum acquisition count to provide diagnosable image quality (DIQ) and investigating the usefulness of preset count acquisition (PCA) for planar images of pediatric 99mTc-dimercaptosuccinic acid (DMSA) scintigraphy. First, we calculated a coefficient of variation (CV) for DIQ with the shortest acquisition time through visual evaluation in 12 pediatric patients who underwent 99mTc-DMSA scintigraphy. Second, a minimum acquisition count to achieve the CV for DIQ was determined with the single regression analysis using CV as an explanatory variable and the total acquisition count as an objective variable in 81 pediatric patients. Finally, we compared PCA images based on the minimum acquisition count and preset time acquisition (PTA) images for 5min in terms of the acquisition time, CV, and renal uptake ratio in another 23 pediatric patients. The visual evaluation showed that the CV corresponding to DIQ with the shortest acquisition time was 27.1%. The total acquisition count corresponding to DIQ was revealed to be 299,764 in the single regression analysis and was determined to be 300,000 after rounding. The CV and its standard deviation in PCA at 300,000 counts and PTA for 5min were 26.4 ± 0.6% and 24.8 ± 1.3%, respectively. The standard deviation of CV in PCA at 300,000 counts was smaller than that in PTA for 5min, indicating little variation in image quality between cases. The acquisition time in PCA at 300,000 counts (3.1 ± 0.7min) was shorter than that in PTA for 5min (5.0 ± 0.0min). The intraclass correlation coefficient between renal uptake ratios for PCA and PTA was 0.98, indicating an extremely high concordance. The minimum acquisition count required for the DIQ was 300,000. In addition, PCA at 300,000 counts was demonstrated to be useful by providing stable image quality at the shortest acquisition time.

Event-Triggered Prescribed Performance Fuzzy Fault-Tolerant Control for Unknown Euler–Lagrange Systems With Any Bounded Initial Values

This paper investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler-Lagrange systems with actuator faults and external disturbances. Firstly, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems (FLSs) are used to approximate unknown parameters of the system. What's more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability (ISS) assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

Nonlinear feedback-based event-triggered output-feedback control for marine surface vehicles under deferred output constraints

This paper investigates the trajectory tracking control for marine surface vehicles (MSVs) in the presence of deferred output constraints, unmeasured velocities, model uncertainties, and unknown external disturbances. A dynamic event-triggered extended state observer (DET-ESO) is proposed to achieve real-time estimation for unmeasurable velocities and lumped disturbances with lower communication cost of measurement channels, and an explicit relationship between the upper bounds of estimation errors and design parameters is provided. Based on the observation results, the control design is conducted using the backstepping approach. Initially, a nonlinear feedback method is employed to empower the control scheme to be self-tuning for tracking errors, thereby improving the dynamic performance of the control system. Subsequently, an error-shifting function and a novel barrier Lyapunov function (BLF) suitable for compound features of nonlinear feedback are used to handle deferred output constraints with unknown initial tracking conditions. On this basis, a dynamic event-triggered mechanism, including dynamic variables and decreasing functions of tracking errors, is presented to evaluate control input signals to dramatically reduce the transmission load and actuator execution rate, and the development of the control scheme is accomplished. Theoretical analysis shows that with the proposed control scheme, all signals in the closed-loop system are uniformly ultimately bounded, and the output constraints violated initially are fulfilled within a preset finite time. Additionally, Zeno behavior is excluded strictly. Finally, the comparative simulation results verify the effectiveness of the scheme.

Practical prescribed-time tracking control for uncertain strict-feedback systems with guaranteed performance under unknown control directions

In this paper, we consider the practical prescribed-time performance guaranteed tracking control problem for a class of uncertain strict-feedback systems subject to unknown control direction. Due to the existence of unknown nonlinearities and uncertainties, it is challenging to design a controller that can ensure the stability of closed-loop system within a predetermined finite time while maintaining the specified transient performance. The underlying problem becomes further complex as the control directions are unknown. To deal with the above problems, a special translation function as well as Nussbaum type function are introduced in the prescribed performance control (PPC) framework. Finally, a PPC as well as preset finite time tracking control scheme is designed, and its effectiveness is confirmed by both theoretical analysis and numerical simulation.

Imitation Learning Based Fast Power System Production Cost Minimization Simulation

Production cost minimization (PCM) simulation is an important tool for long-term power system simulation and assessment. However, solving a PCM problem is always time-consuming for its numerous binary variables. Besides, as modern energy systems have various planning options, the slow solution speed of PCM problems cannot satisfy the requirement of quick assessment of various plans. Most previous works on accelerating PCM problems ignore the importance of accurate solutions on proper assessment but only provide approximate solutions. Therefore, this work provides a fast PCM simulation method with optimality guarantee based on imitation learning. Compared with the popular open-source solver SCIP under default rules, the proposed method can find the optimal solution faster or provide smaller gap when the preset solving time limit hits. Simulation results show the effectiveness of the proposed method.